import torch
import cv2
import numpy as np
import os.path as osp


# class BSDS_Dataset(torch.utils.data.Dataset):
#     def __init__(self, root='data/HED-BSDS', split='test', transform=False):
#         super(BSDS_Dataset, self).__init__()
#         self.root = root
#         self.split = split
#         self.transform = transform
#         if self.split == 'train':
#             self.file_list = osp.join(self.root, 'bsds_pascal_train_pair.lst')
#         elif self.split == 'test':
#             # self.file_list = osp.join(self.root, 'test.lst')
#             self.file_list = osp.join(self.root)
#         else:
#             raise ValueError('Invalid split type!')
#         with open(self.file_list, 'r') as f:
#             self.file_list = f.readlines()
#         self.mean = np.array([104.00698793, 116.66876762, 122.67891434], dtype=np.float32)
#
#     def __len__(self):
#         return len(self.file_list)
#
#     def __getitem__(self, index):
#         if self.split == 'train':
#             img_file, label_file = self.file_list[index].split()
#             label = cv2.imread(osp.join(self.root, label_file), 0)
#             label = np.array(label, dtype=np.float32)
#             label = label[np.newaxis, :, :]
#             label[label == 0] = 0
#             label[np.logical_and(label > 0, label < 127.5)] = 2
#             label[label >= 127.5] = 1
#         else:
#             img_file = self.file_list[index].rstrip()
#
#         img = cv2.imread(osp.join(self.root, img_file))
#         img = np.array(img, dtype=np.float32)
#         img = (img - self.mean).transpose((2, 0, 1))
#
#         if self.split == 'train':
#             return img, label
#         else:
#             return img
import os
import os.path as osp
import numpy as np
import torch
import cv2

class BSDS_Dataset(torch.utils.data.Dataset):
    def __init__(self, root='data/HED-BSDS', split='test', transform=None):
        super(BSDS_Dataset, self).__init__()
        self.root = root
        self.split = split
        self.transform = transform

        if self.split == 'train':
            self.file_list = osp.join(self.root, 'bsds_pascal_train_pair.lst')
        elif self.split == 'test':
            # 读取测试文件夹中的所有图像文件
            self.file_list = [f for f in os.listdir(self.root) if f.endswith(('.jpg', '.png', '.jpeg'))]
        else:
            raise ValueError('Invalid split type! Must be "train" or "test".')

        if self.split == 'train':
            with open(self.file_list, 'r') as f:
                self.file_list = f.readlines()
        else:
            self.file_list = [f for f in self.file_list if f.endswith(('.jpg', '.png', '.jpeg'))]

        self.mean = np.array([104.00698793, 116.66876762, 122.67891434], dtype=np.float32)

    def __len__(self):
        return len(self.file_list)

    def __getitem__(self, index):
        if self.split == 'train':
            img_file, label_file = self.file_list[index].strip().split()
            label = cv2.imread(osp.join(self.root, label_file), 0)
            label = np.array(label, dtype=np.float32)
            label = label[np.newaxis, :, :]
            label[label == 0] = 0
            label[np.logical_and(label > 0, label < 127.5)] = 2
            label[label >= 127.5] = 1
        else:
            img_file = self.file_list[index].rstrip()

        img = cv2.imread(osp.join(self.root, img_file))
        img = np.array(img, dtype=np.float32)
        img = (img - self.mean).transpose((2, 0, 1))

        if self.split == 'train':
            return img, label
        else:
            return img